Tackling Complexity: Process Reconstruction and Graph Transformation for Financial Audits

A key objective of implementing business intelligence tools and methods is to analyze voluminous data and to derive information that would otherwise not be available. Although the overall significance of business intelligence has increased with the general growth of processed and available data it is almost absent in the auditing industry. Public accountants face the challenge to provide an opinion on financial statements that are based on the data produced by the automated processing of countless business transactions in ERP systems. Methods for mining and reconstructing financially relevant process instances can be used as a data analysis tool in the specific context of auditing. In this article we introduce and evaluate an algorithm that effectively reduces the complexity of mined process instances. The presented methods provide a part of the foundation for implementing automated analysis and audit procedures that can assist auditors to perform more efficient and effective audits

Towards Automated Analysis of Business Processes for Financial Audits

Financial audits play a significant role in the economy by safeguarding the correctness of published financial information. Public auditors face the challenge to audit financial statements that are created by increasingly integrated and complex information systems. This paper addresses a specific problem in the auditing process. A major challenge in this process is the analysis and audit of business processes that produce financial entries. We illustrate results from applying business process mining techniques to extensive test and real life data and discuss gained insights from the application for the development of automated business process analysis methods in the context of financial audits

Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits

With the increasing complexity of business processes in today\u27s organizations and the ever-growing amount of structured accounting data, identifying erroneous or fraudulent business transactions and corresponding journal entries poses a major challenge for public accountants at annual audits. In current audit practice, mainly static rules are applied which check only a few attributes of a journal entry for suspicious values. Encouraged by numerous successful adoptions of deep learning in various domains we suggest an approach for applying autoencoder neural networks to detect unusual journal entries within individual financial accounts. The identified journal entries are compared to a list of entries that were manually tagged by two experienced auditors. The comparison shows high f-scores and high recall for all analyzed financial accounts. Additionally, the autoencoder identifies anomalous journal entries that have been overlooked by the auditors. The results underpin the applicability and usefulness of deep learning techniques in financial statement audits

RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations

Detecting accounting anomalies is a recurrent challenge in financial statement audits. Recently, novel methods derived from Deep-Learning (DL) have been proposed to audit the large volumes of a statement's underlying accounting records. However, due to their vast number of parameters, such models exhibit the drawback of being inherently opaque. At the same time, the concealing of a model's inner workings often hinders its real-world application. This observation holds particularly true in financial audits since auditors must reasonably explain and justify their audit decisions. Nowadays, various Explainable AI (XAI) techniques have been proposed to address this challenge, e.g., SHapley Additive exPlanations (SHAP). However, in unsupervised DL as often applied in financial audits, these methods explain the model output at the level of encoded variables. As a result, the explanations of Autoencoder Neural Networks (AENNs) are often hard to comprehend by human auditors. To mitigate this drawback, we propose (RESHAPE), which explains the model output on an aggregated attribute-level. In addition, we introduce an evaluation framework to compare the versatility of XAI methods in auditing. Our experimental results show empirical evidence that RESHAPE results in versatile explanations compared to state-of-the-art baselines. We envision such attribute-level explanations as a necessary next step in the adoption of unsupervised DL techniques in financial auditing.Comment: 9 pages, 4 figures, 5 tables, preprint version, currently under revie

THE ACCESS TO JUSTICE IN SYNERGIZING PAYMENT OBLIGATIONS OF SPECIAL MINING BUSINESS LICENSE HOLDERS WITH TAX COMPLIANCE IN INDONESIA

The obligations of the Special Mining Business License (IUPK) holders to the Government are only given without the active participation of the DGT and the central and local governments to test IUPK compliance as taxpayers. These obligations have the potential to ignore Article 33 and Article 23A of the 1945 Constitution. Based on the normative juridical study using the access to justice and the sustainable development approach, two conclusions are drawn. First, the enactment of several regulations shows that the IUPK's obligation to pay 4% to the Central Government and 6% to the Regional Government from net profits is only given, based on financial reports that have been audited by a public accountant. Second, Article 129 of the Minerba Law, Article 4 (1), Article 6 (1), and Article 9 (1) of the Income Tax Law, as well as Article 15 (3) of PP No. 37 of 2018 must be implemented through monitoring, evaluation, and regular audits of net profits before taxable income on the actual self-assessment reporting conducted by IUPK. It is proposed to make joint audit rules in testing compliance with good mining practice and IUPK obligations, as well as compliance of filling and actual payment of taxes owed

Research on the implication of artificial intelligence in accounting subfields: current research trends from bibliometric analysis, and research directions

All stakeholders recognize the importance of the information provided by various accounting subfields in the decision-making process and managerial activities, on the other hand, with the exponential growth of artificial intelligence, the traditional way of working in accounting has changed, and research about it has been undertaken worldwide, In this context, This study provides a bibliometric analysis of 931 articles which were published from 1990 to 2022 to look for the research trends and most prominent topics and theme addressed in the literature regarding the application of artificial intelligence technologies in five accounting subfields namely Financial Accounting, Management Accounting, Tax Accounting,  Auditing, and Governmental Accounting. Using VOS viewer software, this study contributes to accounting literature by analyzing the current common theme in the literature through visualizing and mapping the occurrence and the co-occurrence of authors’ keywords of 931 articles that address this topic, which will allow us to highlight some less explored avenues of research that can therefore be further explored by scholars. The results show that Financial Accounting is the most commonly researched accounting area explored. The theme most frequently addressed is the detection of financial statement fraud. There were few articles discussing Artificial Intelligence’s implication on Tax Accounting and Government Accounting. Further, the study provided six major areas that have been revealed for future research on this topic: the implication of the Internet of Things, Blockchain and Big Data and the Accounting field, Accounting cybersecurity in the artificial intelligence area, XBRL, and Artificial Intelligence in Accounting.   Keywords: Bibliometric, Accounting subfields, Artificial Intelligence, Vosviewer.                                                                JEL Classification: M4, Q55 Paper type: Theoretical Research All stakeholders recognize the importance of the information provided by various accounting subfields in the decision-making process and managerial activities, on the other hand, with the exponential growth of artificial intelligence, the traditional way of working in accounting has changed, and research about it has been undertaken worldwide, In this context, This study provides a bibliometric analysis of 931 articles which were published from 1990 to 2022 to look for the research trends and most prominent topics and theme addressed in the literature regarding the application of artificial intelligence technologies in five accounting subfields namely Financial Accounting, Management Accounting, Tax Accounting,  Auditing, and Governmental Accounting. Using VOS viewer software, this study contributes to accounting literature by analyzing the current common theme in the literature through visualizing and mapping the occurrence and the co-occurrence of authors’ keywords of 931 articles that address this topic, which will allow us to highlight some less explored avenues of research that can therefore be further explored by scholars. The results show that Financial Accounting is the most commonly researched accounting area explored. The theme most frequently addressed is the detection of financial statement fraud. There were few articles discussing Artificial Intelligence’s implication on Tax Accounting and Government Accounting. Further, the study provided six major areas that have been revealed for future research on this topic: the implication of the Internet of Things, Blockchain and Big Data and the Accounting field, Accounting cybersecurity in the artificial intelligence area, XBRL, and Artificial Intelligence in Accounting.   Keywords: Bibliometric, Accounting subfields, Artificial Intelligence, Vosviewer.                                                                JEL Classification: M4, Q55 Paper type: Theoretical Research&nbsp

Public investments in Croatia

This Occasional Paper occurred as the byproduct of preparations for writing the report by Geoff Dixon, Katarina Ott and Jean-Jacques Dethier “Capital Expenditure by the Government in Croatia: Fiscal Accounts, Budgetary Institutions and Budgeting Process”, The World Bank, Europe and Central Asia Region, Poverty Reduction and Economic Management Unit, June 1998. The authors, Katarina Ott and Anto Bajo (Institute of Public Finance) first published the text in Croatian in the Institute’s journal “Financijska praksa” Volume 23, Number 1, (March 1999). This Occasional Paper is the English language translation of the article published in “Financijska praksa”